Powering Music Rights & Licensing with AI-DLC
How Brain Station 23 used iterative AI-assisted development to accelerate backend, frontend, and admin portal development for an enterprise music rights and licensing platform.
Platforms
Web, User Portal, Admin Portal
Technology Used
Claude Pro, Claude Max, Amazon Q, ChatGPT
Industry
Music Technology, Media & Entertainment
Tags
AI-DLC, Music Technology, Rights Management, Royalty Management, Licensing
75% faster
Measured development time, reduced from 60 days to 15 days
67% faster
Measured debugging time, reduced from 15 days to 5 days
50% faster
Measured test creation, reduced from 20 days to 10 days
3x
Overall delivery speed compared with the project's baseline
33% faster
Measured code review effort, reduced from 15 days to 10 days
Overview
An established music services organization was developing a modern enterprise platform for music rights management, royalty distribution, and music licensing.
The platform enables administrators to manage music rights and distribute royalties while allowing an established user base to purchase music licenses through a secure and scalable system.
Brain Station 23’s development responsibilities covered the backend, frontend, and administration portal, along with features related to rights and royalty management, licensing, user and administrator management, payments, reporting, security, performance, and platform reliability.
AI-DLC was introduced primarily during the software development phase.
Instead of asking AI to generate complete features in one pass, the team used incremental prompting to develop features progressively, review the generated code, test it, identify gaps, and provide additional context until the implementation matched the project’s requirements.
Business Challenges
AI-generated code did not always follow the existing architecture
The platform had its own architecture, coding patterns, and business logic.
AI sometimes generated solutions based on general patterns from its training rather than the application’s established implementation.
The resulting code could appear technically correct while still being inconsistent with the project’s architecture.
Developers therefore had to review and modify generated implementations before they could be integrated.
AI-generated interfaces could ignore the design system
A similar issue appeared in frontend development.
AI could generate interfaces that looked visually appropriate but did not follow the project’s established design tokens, spacing, colours, typography, or component styles.
This required additional prompting and manual corrections.
Business workflows did not always follow conventional patterns
The most notable example was the platform’s order and payment process.
AI initially assumed a conventional e-commerce workflow involving a shopping cart, order management, online payment processing, and a Stripe payment gateway.
That was not how the client’s business operated.
Customers were required to submit payment details manually, upload payment evidence, and provide the payment amount. Administrators then verified the uploaded payment image and amount before approving the order.
The challenge was therefore not getting AI to produce working software. It was getting AI to produce software that represented the client’s actual business process.
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Solution Provided
Incremental AI-assisted development
The team adopted an iterative workflow for feature development.
First, developers defined the required functionality.
AI generated an initial implementation.
Developers then reviewed and tested the output.
If the result did not match the expected behaviour, they supplied additional context, including:
- More specific workflow requirements
- Error messages
- Stack traces
- Screenshots
- Existing source-code context
AI then refined the implementation.
The process was repeated until the feature met the required technical and business standards.
Matching AI to the project’s architecture
Rather than relying on generic prompts, the team progressively provided project-specific source-code context.
This allowed AI-generated implementations to become more aligned with the existing architecture and coding standards.
Correcting the order workflow
For the order-flow example, developers manually reviewed the conventional e-commerce implementation generated by AI.
Unnecessary shopping-cart and online-payment components were removed.
The actual business workflow was then explained through a more detailed prompt.
AI subsequently refined the implementation around manual payment submission and administrator verification.
This ensured that the final functionality represented the client’s actual operating model instead of an assumed industry-standard workflow.
Multiple AI tools for different development needs
Claude Pro was used primarily for backend API development.
Claude Max supported user portal and admin portal development.
Amazon Q was used for backend development and prompt generation.
ChatGPT was used primarily to help formulate prompts.
The combination allowed developers to use different AI tools according to the type of work being performed.
Human validation
AI-generated code remained subject to developer review and testing.
The team also used Tech Lead validation, automated testing, security scanning, screenshots, and error logs to improve the reliability of AI-generated implementations.
This created a controlled development loop rather than an autonomous code-generation process.
Impact
- 75% faster development, reducing the estimated effort from 60 days to 15 days.
- 67% faster debugging, reducing the effort from 15 days to 5 days.
- 50% faster test creation, reducing the effort from 20 days to 10 days.
- 70% faster documentation, reducing the effort from 10 days to 3 days.
- 33% faster code review, reducing the effort from 15 days to 10 days.
- An overall 3x increase in delivery speed compared with the project’s baseline.
AI-Assisted Development With Human Expertise
The project demonstrated that the value of AI-DLC does not come from asking AI to build an entire application independently.
AI was most effective when developers provided clear requirements, relevant project context, and concrete evidence when something went wrong.
The resulting workflow allowed the team to move faster while maintaining control over architecture, business logic, design standards, security, and production quality.
For a platform handling music rights, royalties, licensing, payments, and administration, that balance was critical.
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